Epistasy search in population-based gene mapping using mutual information

Saraee, MH; Nikoofar, H; Manzour, A

Abstract

Gene mapping intends to identify the causal genetic regions of a specific phenotype mostly a complex disease. These diseases are believed to have multiple contributing loci that are potentially unknown and often have subtle patterns making them hard to find. Shannon's mutual information figure is used as a criterion. Algorithms based on this criterion as presented and discussed. Furthermore, an algorithm is proposed to form relevance chains. The proposed algorithms are especially in favor of diseases having almost equally contributing regions known as being epistatic and is applied to both simulated and real data. AMD disease results are included. Some highly associated markers are found in AMD. C# source files for relevance-chains are freely available at https://www. sharemation. com/amanzour.

Citation

Saraee, M., Nikoofar, H., & Manzour, A. (2007, December). Epistasy search in population-based gene mapping using mutual information. Presented at 2007 IEEE International Symposium on Signal Processing and Information Technology, Cairo, EGYPT

• Presentation Conference Type: Other
• Conference Name: 2007 IEEE International Symposium on Signal Processing and Information Technology
• Conference Location: Cairo, EGYPT
• Start Date: Dec 15, 2007
• End Date: Dec 18, 2007
• Publication Date: Jan 1, 2007
• Deposit Date: Oct 26, 2011
• Book Title: 2007 IEEE International Symposium on Signal Processing and Information Technology
• DOI: https://doi.org/10.1109/ISSPIT.2007.4458203
• Publisher URL: http://dx.doi.org/10.1109/ISSPIT.2007.4458203
• Additional Information: Additional Information : Print ISBN: 978-1-4244-1835-0; Event Type : Conference